Understanding the interaction between atmospheric aerosol and water vapour is key in assessing the impacts of anthropogenic influences on the earth's radiative budget, both directly through scattering and absorbing incident solar radiation, and indirectly through changing cloud properties, with considerable uncertainty in the magnitude of the estimated forcings of the latter. Although aerosol particle water uptake is well defined for inorganic compounds, the effects of the aerosol organic fraction on cloud droplet formation and cloud condensation nuclei (CCN) properties are relatively poorly characterised, due to the large number of organic compounds present in atmosphere and their highly complex influences on properties such as water solubility and surface tension.This thesis presents extensive field measurements of CCN/aerosol hygroscopicity from three different environments, together with a novel error model, which has been developed to propagate instrumental uncertainties from measurements in the sub- and supersaturated regimes through to commonly used data products used in large-scale models. This study illustrates that a single hygroscopicity framework is not able to reconcile the measurements within errors, for different measurement environments. The sensitivity of this type of reconciliation study was assessed using several different scenarios, making different assumptions in each case; sensitivity tests using a 'typical' regional aerosol particle water uptake or number-size distribution, demonstrate that it is not possible to apply a constant correction to data to guarantee reconciliation, that the best reconciliation was achieved for size-resolved high-temporal water uptake and aerosol number-size distribution data, and that the application of single-parameter hygroscopicity models requires further examination. It is concluded that high-temporal size-resolved measurements of sub- and supersaturated particle water uptake are fundamental to providing a thorough characterisation of the interaction between atmospheric aerosol and water vapour, and are essential in order to achieve the best possible predictive capability from large-scale models.